Identifiability of Direct Effects from Summary Causal Graphs
- URL: http://arxiv.org/abs/2306.16958v4
- Date: Thu, 15 Feb 2024 16:42:00 GMT
- Title: Identifiability of Direct Effects from Summary Causal Graphs
- Authors: Simon Ferreira and Charles K. Assaad
- Abstract summary: This paper characterizes all cases for which the direct effect is graphically identifiable from a summary causal graph.
It gives two sound finite adjustment sets that can be used to estimate the direct effect whenever it is identifiable.
- Score: 1.0878040851638
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Dynamic structural causal models (SCMs) are a powerful framework for
reasoning in dynamic systems about direct effects which measure how a change in
one variable affects another variable while holding all other variables
constant. The causal relations in a dynamic structural causal model can be
qualitatively represented with an acyclic full-time causal graph. Assuming
linearity and no hidden confounding and given the full-time causal graph, the
direct causal effect is always identifiable. However, in many application such
a graph is not available for various reasons but nevertheless experts have
access to the summary causal graph of the full-time causal graph which
represents causal relations between time series while omitting temporal
information and allowing cycles. This paper presents a complete identifiability
result which characterizes all cases for which the direct effect is graphically
identifiable from a summary causal graph and gives two sound finite adjustment
sets that can be used to estimate the direct effect whenever it is
identifiable.
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